Conventional systems for detecting fraudulent transactions are often utilized after a transaction has taken place. For example, an unauthorized charge for $500 may not be checked for fraudulent activity prior to the charge being approved and the transaction settled. After the transaction is completed it may be flagged by a system as fraudulent based on an unusual location, unusual amount, and/or unusual time of day. As a result of being flagged, a user's card may be disabled.
However, this results in the fraudulent transaction being completed (including an exchange of monetary funds) and the added problem of the user's card being disabled until they are able to get back in touch with the card service provider.
Further, sometimes conventional systems for detecting fraudulent transactions are overly sensitive, and may lead to false positives (i.e., a system detects fraud when there is no fraud), where a user's card is unnecessarily disabled, causing further problems for the user. However, conventional systems for detecting fraudulent transactions may not be capable of learning from the circumstances that led to the false positives. Accordingly, the same type of behavior may continue to trigger a false positive.
At other times, conventional systems for detecting fraudulent transactions are not sensitive enough, and fail to detect fraudulent activity, referred to as a false negative (i.e., a system does not detect fraud when there is fraud). In conventional systems, the fraudulent transaction may be completed, and a user, merchant, or payment processor may not be unable to identify the fraudulent transaction until it is already completed, there is an exchange of monetary funds, and the user sees it on a transaction system.